Business intelligence (BI) processes have become an integral part of many modern organizations. Multidimensional data models and On Line Analytical Processing (OLAP) tools can be vital components of modern BI technologies. Such data models and tools may be used to store and analyze operational, financial, and other metrics of an organization indexed along a multitude of dimensions. An organization can utilize such BI technologies to quickly compare and analyze these metrics in the most relevant context. For example, a chart of financial accounts may represent a standard dimension for a multidimensional financial data model. Each account (e.g., gross revenue or labor cost) may have an associated value, but the value for a particular account may vary over a number of other dimensions, including time, geographic region, corporate division, product, or scenario, for example. Such multidimensional data models, and multidimensional datasets extracted therefrom, are sometimes represented as OLAP cubes, although a three-dimensional cube may be inadequate for representing a data model with 4, 5, 6, or more dimensions.
Conventional data stores may provide flexible data entry functionality by enabling the entry of extended information at the cell level, functionality not found in multidimensional data models. For example, a spreadsheet application may enable the entry of annotations at the cell level so that users can add notes about a piece of data stored in the cell. These notes, however, may not be easily shareable in an enterprise environment. The same spreadsheet must be moved or copied between and among users in order to share annotations. The more users sharing the spreadsheets, the more cumbersome the sharing of annotations can become, especially when considering the needs of an enterprise to limit the ability of certain users to read and/or write to a particular cell.
Other types of extended information may not be possible with existing data models. Additional levels of detail may be desired by, for example, a user of a multidimensional data model who desires a level of numeric detail below the most granular cell level. A stored cell value may be the lowest level at which a model owner wishes to aggregate information. However, the user of the multidimensional data model may wish to associate additional itemizations or line items with the stored value. Such line item details are not available to users of existing multidimensional data models.
It is with respect to these considerations and others that the disclosure made herein is provided.